SoroushVahidi/scidocs huggingface dataset:
Classification and Adaptive Novel Class Detection of Feature-Evolving Data Streams
(2013)

Data stream classification poses many challenges to the data mining community. In this paper, we address four such major challenges, namely, infinite length, concept-drift, concept-evolution, and feature-evolution. Since a data stream is theoretically infinite in length, it is impractical to store and use all the historical data for training. Concept-drift is a common phenomenon in data streams, which occurs as a result of changes in the underlying concepts. Concept-evolution occurs as a result of new classes evolving in the stream. Feature-evolution is a frequently occurring process in many streams, such as text streams, in which new features (i.e., words or phrases) appear as the stream progresses. Most existing data stream classification techniques address only the first two challenges, and ignore the latter two. In this paper, we propose an ensemble classification framework, where each classifier is equipped with a novel class detector, to address concept-drift and concept-evolution. To address feature-evolution, we propose a feature set homogenization technique. We also enhance the novel class detection module by making it more adaptive to the evolving stream, and enabling it to detect more than one novel class at a time. Comparison with state-of-the-art data stream classification techniques establishes the effectiveness of the proposed approach.
A Driving Right Leg Circuit (DgRL) for Improved Common Mode Rejection in Bio-Potential Acquisition Systems
(2016)

The paper presents a novel Driving Right Leg (DgRL) circuit designed to mitigate the effect of common mode signals deriving, say, from power line interferences. The DgRL drives the isolated ground of the instrumentation towards a voltage which is fixed with respect to the common mode potential on the subject, therefore minimizing common mode voltage at the input of the front-end. The paper provides an analytical derivation of the common mode rejection performances of DgRL as compared to the usual grounding circuit or Driven Right Leg (DRL) loop. DgRL is integrated in a bio-potential acquisition system to show how it can reduce the common mode signal of more than 70 dB with respect to standard patient grounding. This value is at least 30 dB higher than the reduction achievable with DRL, making DgRL suitable for single-ended front-ends, like those based on active electrodes. EEG signal acquisition is performed to show how the system can successfully cancel power line interference without any need for differential acquisition, signal post-processing or filtering.
Head Nod Detection from a Full 3D Model
(2015)

As a non-verbal communication mean, head gestures play an important role in face-to-face conversation and recognizing them is therefore of high value for social behavior analysis or Human Robotic Interactions (HRI) modelling. Among the various gestures, head nod is the most common one and can convey agreement or emphasis. In this paper, we propose a novel nod detection approach based on a full 3D face centered rotation model. Compared to previous approaches, we make two contributions. Firstly, the head rotation dynamic is computed within the head coordinate instead of the camera coordinate, leading to pose invariant gesture dynamics. Secondly, besides the rotation parameters, a feature related to the head rotation axis is proposed so that nod-like false positives due to body movements could be eliminated. The experiments on two-party and four-party conversations demonstrate the validity of the approach.
Algorithmic Di erentiation in Python with AlgoPy
(2012)

Many programs for scienti c computing in Python are based on NumPy and therefore make heavy use of numerical linear algebra (NLA) functions, vectorized operations, slicing and broadcasting. AlgoPy provides the means to compute derivatives of arbitrary order and Taylor approximations of such programs. The approach is based on a combination of univariate Taylor polynomial arithmetic and matrix calculus in the (combined) forward/reverse mode of Algorithmic Di erentiation (AD). In contrast to existing AD tools, vectorized operations and NLA functions are not considered to be a sequence of scalar elementary functions. Instead, dedicated algorithms for the matrix product, matrix inverse and the Cholesky, QR, and symmetric eigenvalue decomposition are implemented in AlgoPy. We discuss the reasons for this alternative approach and explain the underlying idea. Examples illustrate how AlgoPy can be used from a user's point of view.
Energy detection spectrum sensing on DPSK modulation transceiver using GNU radio
(2017)

Cognitive radio (CR) can facilitate the issue of range shortage by permitting secondary users to coincide with incumbent clients in authorized range groups, while it causes no interference to occupant interchanges. Spectrum sensing is the essential in enabling innovation of CR to detect the presence of primary users (PUs) or authorized user signals and adventure the spectrum holes. Henceforth, this paper addresses the testing of spectrum sensing method for Differential Phase Shift Keying (DPSK) transceiver by utilizing an open source programming, GNU Radio. Moreover, the effect of various reproduction channels, for example, Dynamic channel demonstrate with Rayleigh fading, dynamic channel display with Rician fading, frequency selective faded channel show with Rayleigh and frequency selective faded channel show with Rician fading are exhibited.
Nerve root sedimentation sign: evaluation of a new radiological sign in lumbar spinal stenosis.
(2010)

STUDY DESIGN
Retrospective case-referent study.


OBJECTIVE
To assess whether the new sedimentation sign discriminates between nonspecific low back pain (LBP) and symptomatic lumbar spinal stenosis (LSS).


SUMMARY OF BACKGROUND DATA
In the diagnosis of LSS, radiologic findings do not always correlate with clinical symptoms, and additional diagnostic signs are needed. In patients without LSS, we observe the sedimentation of lumbar nerve roots to the dorsal part of the dural sac on supine magnetic resonance image scans. In patients with symptomatic and morphologic central LSS, this sedimentation is rarely seen. We named this phenomenon "sedimentation sign" and defined the absence of sedimenting nerve roots as positive sedimentation sign for the diagnosis of LSS.


METHODS
This study included 200 patients. Patients in the LSS group (n = 100) showed claudication with or without LBP and leg pain, a cross-sectional area <80 mm, and a walking distance <200 m; patients in the LBP group (n = 100) had LBP, no leg pain, no claudication, a cross-sectional area of the dural sac >120 mm, and a walking distance >1000 m. The frequency of a positive sedimentation sign was compared between the 2 groups, and intraobserver and interobserver reliability were assessed in a random subsample (n = 20).


RESULTS
A positive sedimentation sign was identified in 94 patients in the LSS group (94%; 95% confidence interval, 90%-99%) but none in the LBP group (0%; 95% confidence interval, 0%-4%). Reliability was kappa = 1.0 (intraobserver) and kappa = 0.93 (interobserver), respectively. There was no difference in the detection of the sign between segmental levels L1-L5 in the LSS group.


CONCLUSION
A positive sedimentation sign exclusively and reliably occurs in patients with LSS, suggesting its usefulness in clinical practice. Future accuracy studies will address its sensitivity and specificity. If they confirm the sign's high specificity, a positive sedimentation sign can rule in LSS, and, with a high sensitivity, a negative sedimentation sign can rule out LSS.
Combining Gradient Boosting Machines with Collective Inference to Predict Continuous Values
(2016)

Gradient boosting of regression trees is a competitive procedure for learning predictive models of continuous data that fits the data with an additive non-parametric model. The classic version of gradient boosting assumes that the data is independent and identically distributed. However, relational data with interdependent, linked instances is now common and the dependencies in such data can be exploited to improve predictive performance. Collective inference is one approach to exploit relational correlation patterns and significantly reduce classification error. However, much of the work on collective learning and inference has focused on discrete prediction tasks rather than continuous. In this work, we investigate how to combine these two paradigms together to improve regression in relational domains. Specifically, we propose a boosting algorithm for learning a collective inference model that predicts a continuous target variable. In the algorithm, we learn a basic relational model, collectively infer the target values, and then iteratively learn relational models to predict the residuals. We evaluate our proposed algorithm on a real network dataset and show that it outperforms alternative boosting methods. However, our investigation also revealed that the relational features interact together to produce better predictions.
Multiple Classifier Systems
(2000)

Ensemble methods are learning algorithms that construct a set of classi ers and then classify new data points by taking a weighted vote of their predictions The original ensemble method is Bayesian aver aging but more recent algorithms include error correcting output coding Bagging and boosting This paper reviews these methods and explains why ensembles can often perform better than any single classi er Some previous studies comparing ensemble methods are reviewed and some new experiments are presented to uncover the reasons that Adaboost does not over t rapidly
Optimizing Java Bytecode Using the Soot Framework: Is It Feasible?
(2000)

This paper presents Soot, a framework for optimizing Java bytecode. The framework is implemented in Java and supports three intermediate representations for representing Java bytecode: Baf, a streamlined representation of Java's stack-based bytecode; Jimple, a typed three-address intermediate representation suitable for optimization; and Grimp, an aggregated version of Jimple. Our approach to class le optimization is to rst convert the stack-based bytecode into Jimple, a three-address form more amenable to traditional program optimization, and then convert the optimized Jimple back to bytecode. In order to demonstrate that our approach is feasible, we present experimental results showing the e ects of processing class les through our framework. In particular, we study the techniques necessary to effectively translate Jimple back to bytecode, without losing performance. Finally, we demonstrate that class le optimization can be quite e ective by showing the results of some basic optimizations using our framework. Our experiments were done on ten benchmarks, including seven SPECjvm98 benchmarks, and were executed on ve di erent Java virtual machine implementations.
Reverse Engineering of the Interaction Diagrams from C++ Code
(2003)

In object oriented programming, the functionalities of a system result from the interactions (message exchanges) among the objects allocated by the system. While designing object interactions is far more complex than designing the object structure in forward engineering, the problem of understanding object interactions during code evolution is even harder, because the related information is spread across the code. In this paper, a technique for the automatic extraction of UML interaction diagrams from C++ code is proposed. The algorithm is based on a static, conservative flow analysis, that approximates the behavior of the system in any execution and for any possible input. Applicability of the approach to large software is achieved by means of two mechanisms: partial analysis and focusing. Usage of our method on a real world, large C++ system confirmed its viability.
A Fast Algorithm for Finding Dominators in a Flowgraph
(1979)

A fast algorithm for finding dominators in a flowgraph is presented. The algorithm uses depth-first search and an efficient method of computing functions defined on paths in trees. A simple implementation of the algorithm runs in <italic>O</italic>(<italic>m</italic> log <italic>n</italic>) time, where <italic>m</italic> is the number of edges and <italic>n</italic> is the number of vertices in the problem graph. A more sophisticated implementation runs in <italic>O</italic>(<italic>m</italic>α(<italic>m</italic>, <italic>n</italic>)) time, where α(<italic>m</italic>, <italic>n</italic>) is a functional inverse of Ackermann's function.
Both versions of the algorithm were implemented in Algol W, a Stanford University version of Algol, and tested on an IBM 370/168. The programs were compared with an implementation by Purdom and Moore of a straightforward <italic>O</italic>(<italic>mn</italic>)-time algorithm, and with a bit vector algorithm described by Aho and Ullman. The fast algorithm beat the straightforward algorithm and the bit vector algorithm on all but the smallest graphs tested.
P300 amplitude is determined by target-to-target interval.
(2002)

P300 event-related brain potential (ERP) measures are affected by target stimulus probability, the number of nontargets preceding the target in the stimulus sequence structure, and interstimulus interval (ISI). Each of these factors contributes to the target-to-target interval (TTI), which also has been found to affect P300. The present study employed a variant of the oddball paradigm and manipulated the number of preceding nontarget stimuli (0, 1, 2, 3) and ISI (1, 2, 4 s) in order to systematically assess TTI effects on P300 values from auditory and visual stimuli. Number of preceding nontargets generally produced stronger effects than ISI in a manner suggesting that TTI determined P300 measures: Amplitude increased as TTI increased for both auditory and visual stimulus conditions, whereas latency tended to decrease with increased TTI. The finding that TTI is a critical determinant of P300 responsivity is discussed within a resource allocation theoretical framework.
Ultra Low Power Wake-Up Radios: A Hardware and Networking Survey
(2017)

In wireless environments, transmission and reception costs dominate system power consumption, motivating research effort on new technologies capable of reducing the footprint of the radio, paving the way for the Internet of Things. The most important challenge is to reduce power consumption when receivers are idle, the so called idle-listening cost. One approach proposes switching off the main receiver, then introduces new wake-up circuitry capable of detecting an incoming transmission, optionally discriminating the packet destination using addressing, then switching on the main radio only when required. This wake-up receiver technology represents the ultimate frontier in low power radio communication. In this paper, we present a comprehensive literature review of the research progress in wake-up radio (WuR) hardware and relevant networking software. First, we present an overview of the WuR system architecture, including challenges to hardware design and a comparison of solutions presented throughout the last decade. Next, we present various medium access control and routing protocols as well as diverse ways to exploit WuRs, both as an extension of pre-existing systems and as a new concept to manage low-power networking.
Experimentable Digital Twins—Streamlining Simulation-Based Systems Engineering for Industry 4.0
(2018)

Digital twins represent real objects or subjects with their data, functions, and communication capabilities in the digital world. As nodes within the internet of things, they enable networking and thus the automation of complex value-added chains. The application of simulation techniques brings digital twins to life and makes them experimentable; digital twins become experimentable digital twins (EDTs). Initially, these EDTs communicate with each other purely in the virtual world. The resulting networks of interacting EDTs model different application scenarios and are simulated in virtual testbeds, providing new foundations for comprehensive simulation-based systems engineering. Its focus is on EDTs, which become more detailed with every single application. Thus, complete digital representations of the respective real assets and their behaviors are created successively. The networking of EDTs with real assets leads to hybrid application scenarios in which EDTs are used in combination with real hardware, thus realizing complex control algorithms, innovative user interfaces, or mental models for intelligent systems.
Design, kinematics and dynamics modeling of a lower-limb walking assistant robot
(2016)

This paper focuses on a lower limb walking assistant robot. This robot has been designed and modeled for two tasks of facilitating healthy people, and assisting patients for walking. First, basic concepts of the motion mechanism are presented. Next, its kinematics and dynamics equations have been developed. Kinematics analysis has been done using Denavit-Hartenberg scheme to obtain position, velocity and acceleration of center of mass (COM) of all links. Dynamics equations have been developed using recursive Lagrange method and finally inverse dynamics problem has been solved. Obtained simulation results will be discussed which validate the proposed model.
Metrics for text entry research: an evaluation of MSD and KSPC, and a new unified error metric
(2003)

We describe and identify shortcomings in two statistics recently introduced to measure accuracy in text entry evaluations: the minimum string distance (MSD) error rate and keystrokes per character (KSPC). To overcome the weaknesses, a new framework for error analysis is developed and demonstrated. It combines the analysis of the presented text, input stream (keystrokes), and transcribed text. New statistics include a unified total error rate, combining two constituent error rates: the corrected error rate (errors committed but corrected) and the not corrected error rate (errors left in the transcribed text). The framework includes other measures including error correction efficiency, participant conscientiousness, utilised bandwidth, and wasted bandwidth. A text entry study demonstrating the new methodology is described.
Lyapunov Approach for the stabilization of the Inverted Spherical Pendulum
(2006)

A nonlinear controller is presented for the stabilization of the spherical inverted pendulum system. The control strategy is based on the Lyapunov approach in conjunction with LaSalle's invariance principle. The proposed controller is able to bring the pendulum to the unstable upright equilibrium point with the position of the movable base at the origin. The obtained closed-loop system has a very large domain of attraction, that can be as large as desired, for any initial position of the pendulum which lies above the horizontal plane
A Data Structure for Dynamic Trees
(1981)

We propose a data structure to maintain a collection of vertex-disjoint trees under a sequence of two kinds of operations: a link operation that combines two trees into one by adding an edge, and a cut operation that divides one tree into two by deleting an edge. Our data structure requires O(log n) time per operation when the time is amortized over a sequence of operations. Using our data structure, we obtain new fast algorithms for the following problems:
 (1) Computing deepest common ancestors.
 (2) Solving various network flow problems including finding maximum flows, blocking flows, and acyclic flows.
 (3) Computing certain kinds of constrained minimum spanning trees.
 (4) Implementing the network simplex algorithm for the transshipment problem.
 Our most significant application is (2); we obtain an O(mn log n)-time algorithm to find a maximum flow in a network of n vertices and m edges, beating by a factor of log n the fastest algorithm previously known for sparse graphs.
A survey of variability modeling in industrial practice
(2013)

Over more than two decades, numerous variability modeling techniques have been introduced in academia and industry. However, little is known about the actual use of these techniques. While dozens of experience reports on software product line engineering exist, only very few focus on variability modeling. This lack of empirical data threatens the validity of existing techniques, and hinders their improvement. As part of our effort to improve empirical understanding of variability modeling, we present the results of a survey questionnaire distributed to industrial practitioners. These results provide insights into application scenarios and perceived benefits of variability modeling, the notations and tools used, the scale of industrial models, and experienced challenges and mitigation strategies.
NONCONVEX MODEL FOR FACTORING NONNEGATIVE MATRICES
(2017)

We study the Nonnegative Matrix Factorization problem which approximates a nonnegative matrix by a low-rank factorization. This problem is particularly important in Machine Learning, and finds itself in a large number of applications. Unfortunately, the original formulation is ill-posed and NPhard. In this paper, we propose a row sparse model based on Row Entropy Minimization to solve the NMF problem under separable assumption which states that each data point is a convex combination of a few distinct data columns. We utilize the concentration of the entropy function and the `∞ norm to concentrate the energy on the least number of latent variables. We prove that under the separability assumption, our proposed model robustly recovers data columns that generate the dataset, even when the data is corrupted by noise. We empirically justify the robustness of the proposed model and show that it is significantly more robust than the state-ofthe-art separable NMF algorithms.
Multi-label classification by exploiting label correlations
(2014)

a Department of Computer Science and Technology, Tongji University, Shanghai 201804, PR China b Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 2G7, Canada c Key Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University, Shanghai 201804, PR China d System Research Institute, Polish Academy of Sciences, Warsaw, Poland e School of Software, Jiangxi Agricultural University, Nanchang 330013, PR China
Variational Bayesian Inference for Big Data Marketing Models 1
(2014)

Hierarchical Bayesian approaches play a central role in empirical marketing as they yield individual-level parameter estimates that can be used for targeting decisions. MCMC methods have been the methods of choice for estimating hierarchical Bayesian models as they are capable of providing accurate individual-level estimates. However, MCMC methods are computationally prohibitive and do not scale well when applied to massive data sets that have become common in the current era of “Big Data”. We introduce to the marketing literature a new class of Bayesian estimation techniques known as variational Bayesian (VB) inference. These methods tackle the scalability challenge via a deterministic optimization approach to approximate the posterior distribution and yield accurate estimates at a fraction of the computational cost associated with simulation-based MCMC methods. We exploit and extend recent developments in variational Bayesian inference and highlight how two VB estimation approaches – Mean-field VB (that is analogous to Gibbs sampling) for conjugate models and Fixed-form VB (which is analogous to Metropolis-Hasting) for nonconjugate models – can be effectively combined for estimating complex marketing models. We also show how recent advances in parallel computing and in stochastic optimization can be used to further enhance the speed of these VB methods. Using simulated as well as real data sets, we apply the VB approaches to several commonly used marketing models (e.g. mixed linear, logit, selection, and hierarchical ordinal logit models), and demonstrate how the VB inference is widely applicable for marketing problems.